COMPUTER VISION

Self-supervised Correspondence Estimation via Multiview Registration

January 03, 2023

Abstract

Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by frame pairs. However, by only relying on close-by frame pairs, those approaches miss out on the richer long-range consistency between distant overlapping frames. To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences. Our approach combines pairwise correspondence estimation and registration with a novel SE(3) transformation synchronization algorithm. Our key insight is that self-supervised multiview registration allows us to obtain correspondences over longer time frames; increasing both the diversity and difficulty of sampled pairs. We evaluate our approach on indoor scenes for correspondence estimation and RGB-D pointcloud registration and find that we perform on-par with supervised approaches.

Download the Paper

AUTHORS

Written by

Benjamin Graham

Andrea Vedaldi

David Novotny

Ignacio Rocco

Justin Johnson

Natalia Neverova

Mohamed El Banani

Publisher

WACV

Research Topics

Computer Vision

Related Publications

March 09, 2023

COMPUTER VISION

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

March 09, 2023

February 21, 2023

COMPUTER VISION

CORE MACHINE LEARNING

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Felix Xu, Fuyuan Zhang, Hua Qi, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Zhijie Wang

February 21, 2023

January 10, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Online Backfilling with No Regret for Large-Scale Image Retrieval

Gokhan Uzunbas, Joena Zhang, Sara Cao, Ser-Nam Lim, Taipeng Tian, Bohyung Han, Seonguk Seo

January 10, 2023

January 04, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Xi Liu, Panganamala Kumar, Ruida Zhou, Tao Liu

January 04, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.